text: # Copyright (c) OpenMMLab. All rights reserved.
  import torch.nn as nn
  import torch.utils.checkpoint as cp
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmcv.runner import BaseModule
  from torch.nn.modules.batchnorm import _BatchNorm
  
  from ..builder import BACKBONES
  from ..utils import ResLayer
  
  
  class BasicBlock(BaseModule):
      expansion = 1
  
      def __init__(self,
                   inplanes,
                   planes,
                   stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   with_cp=False,
                   conv_cfg=None,
                   norm_cfg=dict(type='BN'),
                   init_cfg=None):
          super(BasicBlock, self).__init__(init_cfg)
  
          self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
          self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
  
          self.conv1 = build_conv_layer(
              conv_cfg,
              inplanes,
              planes,
              3,
              stride=stride,
              padding=dilation,
              dilation=dilation,
              bias=False)
          self.add_module(self.norm1_name, norm1)
          self.conv2 = build_conv_layer(
              conv_cfg, planes, planes, 3, padding=1, bias=False)
          self.add_module(self.norm2_name, norm2)
  
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
          self.dilation = dilation
          self.with_cp = with_cp
  
      @property
      def norm1(self):
          """nn.Module: normalization layer after the first convolution layer"""
          return getattr(self, self.norm1_name)
  
      @property
      def norm2(self):
          """nn.Module: normalization layer after the second convolution layer"""
          return getattr(self, self.norm2_name)
  
      def forward(self, x):
          """Forward function."""
  
          def _inner_forward(x):
              identity = x
  
              out = self.conv1(x)
              out = self.norm1(out)
              out = self.relu(out)
  
              out = self.conv2(out)
              out = self.norm2(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out += identity
  
              return out
  
          if self.with_cp and x.requires_grad:
              out = cp.checkpoint(_inner_forward, x)
          else:
              out = _inner_forward(x)
  
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(BaseModule):
      expansion = 4
  
      def __init__(self,
                   inplanes,
                   planes,
                   stride=1,
                   dilation=1,
                   downsample=None,
                   style='pytorch',
                   with_cp=False,
                   conv_cfg=None,
                   norm_cfg=dict(type='BN'),
                   init_cfg=None):
          """Bottleneck block for ResNet.
  
          If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
          it is "caffe", the stride-two layer is the first 1x1 conv layer.
          """
          super(Bottleneck, self).__init__(init_cfg)
          assert style in ['pytorch', 'caffe']
  
          self.inplanes = inplanes
          self.planes = planes
          self.stride = stride
          self.dilation = dilation
          self.style = style
          self.with_cp = with_cp
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
  
          if self.style == 'pytorch':
              self.conv1_stride = 1
              self.conv2_stride = stride
          else:
              self.conv1_stride = stride
              self.conv2_stride = 1
  
          self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
          self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
          self.norm3_name, norm3 = build_norm_layer(
              norm_cfg, planes * self.expansion, postfix=3)
  
          self.conv1 = build_conv_layer(
              conv_cfg,
              inplanes,
              planes,
              kernel_size=1,
              stride=self.conv1_stride,
              bias=False)
          self.add_module(self.norm1_name, norm1)
          self.conv2 = build_conv_layer(
              conv_cfg,
              planes,
              planes,
              kernel_size=3,
              stride=self.conv2_stride,
              padding=dilation,
              dilation=dilation,
              bias=False)
          self.add_module(self.norm2_name, norm2)
          self.conv3 = build_conv_layer(
              conv_cfg,
              planes,
              planes * self.expansion,
              kernel_size=1,
              bias=False)
          self.add_module(self.norm3_name, norm3)
  
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
  
      @property
      def norm1(self):
          """nn.Module: normalization layer after the first convolution layer"""
          return getattr(self, self.norm1_name)
  
      @property
      def norm2(self):
          """nn.Module: normalization layer after the second convolution layer"""
          return getattr(self, self.norm2_name)
  
      @property
      def norm3(self):
          """nn.Module: normalization layer after the third convolution layer"""
          return getattr(self, self.norm3_name)
  
      def forward(self, x):
          """Forward function."""
  
          def _inner_forward(x):
              identity = x
  
              out = self.conv1(x)
              out = self.norm1(out)
              out = self.relu(out)
  
              out = self.conv2(out)
              out = self.norm2(out)
              out = self.relu(out)
  
              out = self.conv3(out)
              out = self.norm3(out)
  
              if self.downsample is not None:
                  identity = self.downsample(x)
  
              out += identity
  
              return out
  
          if self.with_cp and x.requires_grad:
              out = cp.checkpoint(_inner_forward, x)
          else:
              out = _inner_forward(x)
  
          out = self.relu(out)
  
          return out
  
  
  @BACKBONES.register_module()
  class ResNet(BaseModule):
      """ResNet backbone.
  
      Args:
          depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
          in_channels (int): Number of input image channels. Defaults to 3.
          stem_channels (int | None): Number of stem channels. If not specified,
              it will be the same as `base_channels`. Defaults to None.
          base_channels (int): Number of base channels of res layer.
              Defaults to 64.
          num_stages (int): Resnet stages. Defaults to 4.
          strides (Sequence[int]): Strides of the first block of each stage.
          dilations (Sequence[int]): Dilation of each stage.
          out_indices (Sequence[int]): Output from which stages.
          style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
              layer is the 3x3 conv layer, otherwise the stride-two layer is
              the first 1x1 conv layer.
          deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
              Defaults to False.
          avg_down (bool): Use AvgPool instead of stride conv when
              downsampling in the bottleneck. Defaults to False.
          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
              -1 means not freezing any parameters.
          norm_cfg (dict): dictionary to construct and config norm layer.
          norm_eval (bool): Whether to set norm layers to eval mode, namely,
              freeze running stats (mean and var). Note: Effect on Batch Norm
              and its variants only.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed.
          zero_init_residual (bool): whether to use zero init for last norm layer
              in resblocks to let them behave as identity.
          init_cfg (dict or list[dict], optional): Initialization config dict.
              Defaults to None
  
      Example:
          >>> from mmselfsup.models import ResNet
          >>> import torch
          >>> self = ResNet(depth=18)
          >>> self.eval()
          >>> inputs = torch.rand(1, 3, 32, 32)
          >>> level_outputs = self.forward(inputs)
          >>> for level_out in level_outputs:
          ...     print(tuple(level_out.shape))
          (1, 64, 8, 8)
          (1, 128, 4, 4)
          (1, 256, 2, 2)
          (1, 512, 1, 1)
      """
  
      arch_settings = {
          18: (BasicBlock, (2, 2, 2, 2)),
          34: (BasicBlock, (3, 4, 6, 3)),
          50: (Bottleneck, (3, 4, 6, 3)),
          101: (Bottleneck, (3, 4, 23, 3)),
          152: (Bottleneck, (3, 8, 36, 3))
      }
  
      def __init__(self,
                   depth,
                   in_channels=3,
                   stem_channels=None,
                   base_channels=64,
                   num_stages=4,
                   strides=(1, 2, 2, 2),
                   dilations=(1, 1, 1, 1),
                   out_indices=(0, 1, 2, 3, 4),
                   style='pytorch',
                   deep_stem=False,
                   avg_down=False,
                   frozen_stages=-1,
                   conv_cfg=None,
                   norm_cfg=dict(type='BN', requires_grad=True),
                   norm_eval=False,
                   with_cp=False,
                   zero_init_residual=False,
                   init_cfg=None):
          super(ResNet, self).__init__(init_cfg)
          self.zero_init_residual = zero_init_residual
          if depth not in self.arch_settings:
              raise KeyError(f'invalid depth {depth} for resnet')
  
          block_init_cfg = None
          if init_cfg is None:
              self.init_cfg = [
                  dict(type='Kaiming', layer='Conv2d'),
                  dict(
                      type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
              ]
              block = self.arch_settings[depth][0]
              if self.zero_init_residual:
                  if block is BasicBlock:
                      block_init_cfg = dict(
                          type='Constant', val=0, override=dict(name='norm2'))
                  elif block is Bottleneck:
                      block_init_cfg = dict(
                          type='Constant', val=0, override=dict(name='norm3'))
  
          self.depth = depth
          if stem_channels is None:
              stem_channels = base_channels
          self.stem_channels = stem_channels
          self.base_channels = base_channels
          self.num_stages = num_stages
          assert num_stages >= 1 and num_stages <= 4
          self.strides = strides
          self.dilations = dilations
          assert len(strides) == len(dilations) == num_stages
          self.out_indices = out_indices
          assert max(out_indices) < num_stages + 1
          self.style = style
          self.deep_stem = deep_stem
          self.avg_down = avg_down
          self.frozen_stages = frozen_stages
          self.conv_cfg = conv_cfg
          self.norm_cfg = norm_cfg
          self.with_cp = with_cp
          self.norm_eval = norm_eval
          self.zero_init_residual = zero_init_residual
          self.block, stage_blocks = self.arch_settings[depth]
          self.stage_blocks = stage_blocks[:num_stages]
          self.inplanes = stem_channels
  
          self._make_stem_layer(in_channels, stem_channels)
  
          self.res_layers = []
          for i, num_blocks in enumerate(self.stage_blocks):
              stride = strides[i]
              dilation = dilations[i]
              planes = base_channels * 2**i
              res_layer = self.make_res_layer(
                  block=self.block,
                  inplanes=self.inplanes,
                  planes=planes,
                  num_blocks=num_blocks,
                  stride=stride,
                  dilation=dilation,
                  style=self.style,
                  avg_down=self.avg_down,
                  with_cp=with_cp,
                  conv_cfg=conv_cfg,
                  norm_cfg=norm_cfg,
                  init_cfg=block_init_cfg)
              self.inplanes = planes * self.block.expansion
              layer_name = f'layer{i + 1}'
              self.add_module(layer_name, res_layer)
              self.res_layers.append(layer_name)
  
          self._freeze_stages()
  
          self.feat_dim = self.block.expansion * base_channels * 2**(
              len(self.stage_blocks) - 1)
  
      def make_res_layer(self, **kwargs):
          """Pack all blocks in a stage into a ``ResLayer``."""
          return ResLayer(**kwargs)
  
      @property
      def norm1(self):
          """nn.Module: the normalization layer named "norm1" """
          return getattr(self, self.norm1_name)
  
      def _make_stem_layer(self, in_channels, stem_channels):
          if self.deep_stem:
              self.stem = nn.Sequential(
                  build_conv_layer(
                      self.conv_cfg,
                      in_channels,
                      stem_channels // 2,
                      kernel_size=3,
                      stride=2,
                      padding=1,
                      bias=False),
                  build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
                  nn.ReLU(inplace=True),
                  build_conv_layer(
                      self.conv_cfg,
                      stem_channels // 2,
                      stem_channels // 2,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=False),
                  build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
                  nn.ReLU(inplace=True),
                  build_conv_layer(
                      self.conv_cfg,
                      stem_channels // 2,
                      stem_channels,
                      kernel_size=3,
                      stride=1,
                      padding=1,
                      bias=False),
                  build_norm_layer(self.norm_cfg, stem_channels)[1],
                  nn.ReLU(inplace=True))
          else:
              self.conv1 = build_conv_layer(
                  self.conv_cfg,
                  in_channels,
                  stem_channels,
                  kernel_size=7,
                  stride=2,
                  padding=3,
                  bias=False)
              self.norm1_name, norm1 = build_norm_layer(
                  self.norm_cfg, stem_channels, postfix=1)
              self.add_module(self.norm1_name, norm1)
              self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
      def _freeze_stages(self):
          if self.frozen_stages >= 0:
              if self.deep_stem:
                  self.stem.eval()
                  for param in self.stem.parameters():
                      param.requires_grad = False
              else:
                  self.norm1.eval()
                  for m in [self.conv1, self.norm1]:
                      for param in m.parameters():
                          param.requires_grad = False
  
          for i in range(1, self.frozen_stages + 1):
              m = getattr(self, f'layer{i}')
              m.eval()
              for param in m.parameters():
                  param.requires_grad = False
  
      def forward(self, x):
          """Forward function."""
          if self.deep_stem:
              x = self.stem(x)
          else:
              x = self.conv1(x)
              x = self.norm1(x)
              x = self.relu(x)  # r50: 64x128x128
          outs = []
          if 0 in self.out_indices:
              outs.append(x)
          x = self.maxpool(x)  # r50: 64x56x56
          for i, layer_name in enumerate(self.res_layers):
              res_layer = getattr(self, layer_name)
              x = res_layer(x)
              if i + 1 in self.out_indices:
                  outs.append(x)
          # r50: 1-256x56x56; 2-512x28x28; 3-1024x14x14; 4-2048x7x7
          return tuple(outs)
  
      def train(self, mode=True):
          """Convert the model into training mode while keep normalization layer
          freezed."""
          super(ResNet, self).train(mode)
          self._freeze_stages()
          if mode and self.norm_eval:
              for m in self.modules():
                  # trick: eval have effect on BatchNorm only
                  if isinstance(m, _BatchNorm):
                      m.eval()
  
  
  @BACKBONES.register_module()
  class ResNetV1d(ResNet):
      r"""ResNetV1d variant described in `Bag of Tricks
      <https://arxiv.org/pdf/1812.01187.pdf>`_.
  
      Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
      the input stem with three 3x3 convs. And in the downsampling block, a 2x2
      avg_pool with stride 2 is added before conv, whose stride is changed to 1.
      """
  
      def __init__(self, **kwargs):
          super(ResNetV1d, self).__init__(
              deep_stem=True, avg_down=True, **kwargs)
  